This series of files compile analyses done during Chapter 2.

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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1. Ecological Quality Status

When relevant, we calculated an Ecological Quality Ratio (EQR) as established by the WFD and MSFD (which varies between 0 and 1). This ratio is calculated with the following equation:

\[ EQR = \frac{V_{ind} - R_{bad}}{R_{good} - R_{bad}} \]

  • \(V_{ind}\) is the value of an indicator at a certain location
  • \(R_{bad}\) is the reference value for a “bad” status
  • \(R_{good}\) is the reference value for a “good” status

This ratio is then classed into Ecological Quality Status (EQS) categories, where reference values and limits for class transitions are specific to each indicator. Five classes are typically described:

  • bad (red #FF0000)
  • poor (orange #FFA500)
  • moderate (yellow #EEEE00)
  • good (green #228B22)
  • high (blue #0000EE)

We calculated this ratio using different indicators, in order to compare their efficiency and relevance.

AMBI

We defined class thresholds using the methods from Borja et al. (2000) and Muxika et al. (2005).

M-AMBI

We defined class thresholds using the method from Muxika et al. (2007).

BENTIX

We defined class thresholds using the method from Simboura & Zenetos (2002).

BOPA

We defined class thresholds using the method from Dauvin & Ruellet (2007).

2. Non-metric Multidimensional Scaling

Species densities have been transformed with a (log+1) operation. Green points are stations within the bay, red points are within the archipelago.

Specific richness

Total density

Total biomass

W-Statistic

Shannon index

Margalef index

Simpson index

Pielou evenness

Taxonomic diversity

Functional richness

Functional evenness

Functional divergence

AMBI

M-AMBI

BENTIX

BOPA

3. Relationships between indicators and abiotic parameters

In this section, we study the statistical relationships between indicators calculated above and different abiotic parameters, in order to understand how well they can be used to detect perturbations.

3.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

3.1.1. Shallow stations

Specific richness

Total density

Total biomass

W-Statistic

Shannon index

Margalef index

Simpson index

Pielou evenness

Taxonomic diversity

Functional richness

Functional evenness

Functional divergence

AMBI

M-AMBI

BENTIX

BOPA

3.1.2. Deep stations

Specific richness

Total density

Total biomass

W-Statistic

Shannon index

Margalef index

Simpson index

Pielou evenness

Taxonomic diversity

Functional richness

Functional evenness

Functional divergence

AMBI

M-AMBI

BENTIX

BOPA

3.2. Correlation

Correlations have been calculated with Spearman’s rank coefficients.

3.2.1. Shallow stations

Correlation coefficients between habitat parameters and indices for shallow stations
  S N B W H margalef lambda J delta FR FE FD AMBI M_AMBI BENTIX BOPA
om 0.107 -0.073 0.089 0.089 0.089 0.187 -0.098 0.013 0.022 -0.036 -0.004 0.092 -0.432 0.311 0.449 0.347
gravel -0.024 0.096 0.055 -0.026 0.071 -0.005 0.196 0.143 0.155 0.223 0.105 -0.194 0.319 -0.151 -0.365 -0.052
sand 0.229 0.245 -0.092 -0.016 0.104 0.082 0.095 -0.118 0.009 0.234 -0.108 0.252 0.471 -0.051 -0.332 -0.273
silt -0.136 -0.181 0.141 0.041 -0.071 -0.03 -0.117 0.049 -0.027 -0.182 0.105 -0.136 -0.474 0.132 0.352 0.252
clay -0.202 -0.179 0.045 0.021 0.048 -0.181 0.168 0.213 0.046 -0.066 0.085 -0.26 -0.119 -0.122 0.008 -0.023
arsenic -0.474 -0.456 -0.423 -0.1 -0.113 -0.182 0.038 0.283 0.225 -0.428 -0.023 -0.595 -0.014 -0.336 -0.124 0.015
cadmium -0.196 -0.076 -0.416 -0.401 -0.182 -0.152 -0.174 -0.066 -0.059 -0.146 -0.301 -0.156 -0.171 -0.11 -0.013 -0.077
chromium -0.359 -0.522 -0.591 0.022 0.033 -0.053 0.06 0.287 0.225 -0.498 0.031 -0.351 0.006 -0.141 -0.019 -0.017
copper -0.327 -0.546 -0.506 0.138 0.05 -0.018 0.068 0.315 0.23 -0.426 0.059 -0.372 0.092 -0.174 0.006 0.011
iron -0.309 -0.49 -0.391 0.145 0.054 -0.006 0.075 0.269 0.262 -0.474 0.083 -0.287 -0.061 -0.103 0.063 -0.037
manganese -0.276 -0.306 -0.531 -0.109 -0.047 -0.073 -0.017 0.092 0.099 -0.459 -0.037 -0.269 0.016 -0.12 0.028 0.041
mercury -0.392 -0.517 -0.355 0.063 0.039 -0.113 0.109 0.423 0.26 -0.499 0.132 -0.379 -0.112 -0.119 0.056 0.021
lead -0.381 -0.549 -0.499 0.067 0.022 -0.034 0.086 0.365 0.237 -0.42 0.062 -0.4 -0.013 -0.174 0.037 0.148
zinc -0.343 -0.515 -0.613 0.029 0.058 -0.037 0.097 0.32 0.259 -0.465 -0.011 -0.411 0.063 -0.172 -0.031 -0.032
p-values of correlation test between habitat parameters and indices for shallow stations
  S N B W H margalef lambda J delta FR FE FD AMBI M_AMBI BENTIX BOPA
om 0.6042 0.7237 0.6661 0.6636 0.6636 0.3587 0.6323 0.9484 0.9175 0.8605 0.9841 0.6537 0.02864 0.1222 0.02133 0.08199
gravel 0.9083 0.6414 0.791 0.8982 0.7293 0.9803 0.3377 0.4863 0.4493 0.2741 0.6109 0.3434 0.1122 0.461 0.06668 0.7995
sand 0.2613 0.228 0.6549 0.9392 0.6145 0.6902 0.6429 0.5648 0.9643 0.249 0.5987 0.2145 0.01519 0.8035 0.09799 0.1778
silt 0.5064 0.3755 0.4914 0.8422 0.7286 0.884 0.5683 0.8112 0.8958 0.3732 0.6092 0.5079 0.01437 0.5192 0.07773 0.2134
clay 0.3216 0.3828 0.8286 0.918 0.8163 0.3756 0.413 0.2965 0.8224 0.7472 0.68 0.1992 0.5637 0.5511 0.9705 0.9109
arsenic 0.01449 0.01931 0.03116 0.6272 0.5839 0.3727 0.8547 0.1608 0.2693 0.02914 0.9105 0.001346 0.9443 0.09292 0.5461 0.9406
cadmium 0.337 0.7107 0.03431 0.04234 0.3728 0.4574 0.3962 0.7489 0.7743 0.4763 0.1345 0.4471 0.4042 0.5918 0.9498 0.7077
chromium 0.07201 0.006246 0.001481 0.9142 0.8722 0.7971 0.7703 0.1547 0.2691 0.009577 0.88 0.07851 0.9762 0.4924 0.926 0.9362
copper 0.1029 0.003903 0.00909 0.4983 0.8098 0.9308 0.7407 0.1171 0.257 0.02999 0.7728 0.06128 0.6539 0.3961 0.9775 0.958
iron 0.1247 0.01112 0.0489 0.4771 0.7917 0.9787 0.7155 0.1831 0.1949 0.01445 0.6878 0.1548 0.7686 0.618 0.7587 0.859
manganese 0.172 0.1279 0.005904 0.5945 0.8202 0.723 0.9334 0.6549 0.6275 0.01828 0.8592 0.1837 0.9388 0.5603 0.8905 0.8406
mercury 0.04779 0.006883 0.0762 0.7584 0.8516 0.5806 0.5945 0.03148 0.1986 0.009534 0.5204 0.0563 0.5852 0.5626 0.7842 0.9176
lead 0.05509 0.003659 0.009392 0.7461 0.9141 0.8709 0.6754 0.06698 0.2429 0.03246 0.7638 0.04293 0.9484 0.3944 0.8577 0.4699
zinc 0.08579 0.007047 0.0008679 0.8866 0.7792 0.8566 0.6358 0.1105 0.2023 0.01656 0.959 0.03695 0.7601 0.4017 0.8813 0.8774

3.2.2. Deep stations

Correlation coefficients between habitat parameters and indices for deep stations
  S N B W H margalef lambda J delta FR FE FD AMBI M_AMBI BENTIX BOPA
om -0.248 -0.101 -0.037 -0.155 -0.13 -0.263 -0.047 0.018 -0.149 -0.206 -0.205 0.009 -0.092 -0.157 0.266 0.203
gravel 0.123 -0.013 0.205 0.239 0.076 0.146 0.061 0.058 0.116 0.25 0.095 -0.115 -0.085 0.118 -0.055 -0.023
sand 0.126 0.004 -0.015 0.194 0.179 0.181 0.142 0.111 0.232 0.045 0.178 0.034 0.06 0.15 -0.263 -0.309
silt -0.106 0.072 -0.019 -0.219 -0.202 -0.187 -0.193 -0.195 -0.286 -0.09 -0.208 0.034 -0.035 -0.139 0.231 0.341
clay -0.004 -0.019 -0.015 -0.021 -0.007 0.011 -0.013 -0.06 -0.044 0.126 -0.092 0.011 -0.034 -0.024 0.041 0.104
arsenic -0.27 -0.031 -0.079 -0.288 -0.285 -0.317 -0.223 -0.169 -0.291 -0.254 -0.204 0.207 -0.048 -0.24 0.225 0.331
cadmium -0.317 -0.079 -0.077 -0.294 -0.289 -0.333 -0.233 -0.154 -0.322 -0.317 -0.184 0.283 0.04 -0.295 0.137 0.276
chromium -0.311 -0.078 0.034 -0.267 -0.329 -0.357 -0.266 -0.174 -0.353 -0.287 -0.221 0.198 -0.026 -0.295 0.235 0.36
copper -0.318 -0.069 0.006 -0.235 -0.285 -0.365 -0.222 -0.169 -0.348 -0.322 -0.252 0.293 -0.06 -0.266 0.254 0.31
iron -0.449 -0.228 0.072 -0.266 -0.363 -0.454 -0.28 -0.094 -0.353 -0.363 -0.189 0.182 0.035 -0.379 0.098 0.328
manganese -0.308 -0.079 0.049 -0.258 -0.313 -0.36 -0.254 -0.163 -0.344 -0.285 -0.275 0.192 -0.03 -0.277 0.196 0.376
mercury -0.217 0.019 0.05 -0.203 -0.251 -0.278 -0.22 -0.212 -0.329 -0.283 -0.267 0.287 -0.027 -0.205 0.231 0.297
lead -0.301 -0.041 -0.024 -0.278 -0.306 -0.347 -0.256 -0.195 -0.343 -0.273 -0.238 0.283 0.01 -0.282 0.193 0.334
zinc -0.342 -0.069 -0.053 -0.286 -0.308 -0.379 -0.241 -0.182 -0.349 -0.326 -0.218 0.335 0.004 -0.306 0.18 0.303
p-values of correlation test between habitat parameters and indices for deep stations
  S N B W H margalef lambda J delta FR FE FD AMBI M_AMBI BENTIX BOPA
om 0.02459 0.3675 0.7435 0.1635 0.2456 0.01699 0.6754 0.8707 0.1826 0.0639 0.06448 0.9389 0.4133 0.158 0.01573 0.06805
gravel 0.2716 0.9053 0.06425 0.03055 0.4958 0.1918 0.5857 0.6039 0.2993 0.02327 0.3945 0.3057 0.4482 0.2916 0.6212 0.8381
sand 0.2612 0.9702 0.8968 0.08083 0.1078 0.1033 0.2026 0.3188 0.03598 0.69 0.1086 0.7624 0.5932 0.1775 0.01718 0.004724
silt 0.3426 0.5178 0.8642 0.04799 0.06857 0.09176 0.08247 0.07956 0.009136 0.4216 0.06117 0.7603 0.7533 0.2128 0.03641 0.001735
clay 0.9691 0.868 0.8947 0.8486 0.9523 0.9206 0.9067 0.5947 0.6935 0.2609 0.411 0.922 0.7611 0.833 0.714 0.3521
arsenic 0.01399 0.7815 0.4799 0.008696 0.009455 0.003656 0.04401 0.1283 0.007928 0.02149 0.06595 0.06212 0.6656 0.02989 0.04228 0.002378
cadmium 0.003699 0.4801 0.4899 0.00741 0.008482 0.002217 0.03551 0.1676 0.003198 0.003684 0.09801 0.01005 0.7245 0.007097 0.2204 0.01204
chromium 0.004395 0.4853 0.7593 0.01533 0.002539 0.0009817 0.01583 0.1185 0.00114 0.008854 0.04601 0.07398 0.8178 0.007079 0.03379 0.0008835
copper 0.00356 0.5384 0.9568 0.03393 0.009581 0.0007434 0.04513 0.1294 0.001377 0.003164 0.02234 0.007541 0.5946 0.01584 0.02136 0.004539
iron 2.357e-05 0.0391 0.5209 0.01611 0.0008703 1.821e-05 0.01105 0.3981 0.001222 0.0008618 0.08931 0.1023 0.7543 0.0004477 0.3805 0.002633
manganese 0.004816 0.4802 0.6632 0.01965 0.004388 0.0008973 0.0214 0.1436 0.001634 0.009644 0.01279 0.08341 0.7877 0.01168 0.07685 0.0004928
mercury 0.0502 0.8681 0.6525 0.06723 0.02294 0.01157 0.0472 0.05604 0.002681 0.01032 0.01564 0.009027 0.8123 0.06473 0.03687 0.006739
lead 0.005972 0.7116 0.8275 0.01159 0.005188 0.001414 0.0201 0.07908 0.001615 0.01299 0.03097 0.01008 0.9308 0.01013 0.08155 0.002162
zinc 0.001654 0.5373 0.6367 0.009233 0.00481 0.0004453 0.02904 0.1017 0.001332 0.002824 0.04918 0.002117 0.9743 0.005254 0.1064 0.005722

4. Bootstrap for estimating indicator robustness

In this section, we are calculating values of the indicators for shallow and deep stations using a bootstrap method, so that we have an idea of the robustness of each measure.

4.1. Shallow stations

Bootstrap results for shallow stations
  True mean Bootstrap Mean bias Boostrap 95% CI
S 9.192 -0.03569 [9.1708;9.2852]
N 138.7 -0.3769 [136.8829;141.2555]
B 7.352 -0.05717 [7.1399;7.6789]
W 0.0109 -0.0164 [0.0269;0.0277]
H 1.353 -0.006664 [1.3537;1.3663]
margalef 1.926 -0.01379 [1.9302;1.949]
lambda 0.6202 -0.002792 [0.6205;0.6255]
J 0.6564 -0.00366 [0.6572;0.663]
delta 51.66 -0.3568 [51.7936;52.2472]
FR 23.35 -3.171 [26.111;26.9268]
FE 0.5542 0.002318 [0.5495;0.5543]
FD 0.7656 -0.007396 [0.7701;0.7759]

4.2. Deep stations

Bootstrap results for deep stations
  True mean Bootstrap Mean bias Boostrap 95% CI
S 13.99 -0.009295 [13.964;14.0302]
N 89.13 -0.1556 [88.8068;89.7726]
B 8.72 0.0243 [8.5538;8.838]
W 0.02522 -0.007476 [0.0325;0.0329]
H 1.952 0.0002019 [1.9489;1.9551]
margalef 3.046 -7.372e-05 [3.0402;3.052]
lambda 0.7699 0.0001827 [0.7687;0.7707]
J 0.7601 0.0003165 [0.7588;0.7608]
delta 63.48 0.01433 [63.386;63.5518]
FR 31.76 -7.59 [38.827;39.8772]
FE 0.6324 -0.001514 [0.6331;0.6347]
FD 0.8282 0.01091 [0.8165;0.8181]

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